Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using average MPG and another category that you feel may influence fuel efficiency. Answer the following questions.
Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot?
Answer: We chose to compare average MPG and weight because we thought that the heavier the car the less the MPG would be.
Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: X-axis is weight; y-axis is Average MPG. Weight is the independent variable and average MPG would be the variable that would be affected by the weight, thus the dependent variable.
Do you believe there is a relationship between the two categories? Why or why not?
Answer: Yes because as the weight increases, the average MPG is generally decreasing.
If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Answer: Negative slope. This means that as one category increases the other category decreases.
Create the linear regession equation in Excel, which Excel calls the trend line. Click the boxes to create both the equation and the r2 value on the graph. Answer the following questions.
What is your regression equation? Explain what the equation means in words.
Answer: y = -0.007x + 49.32; The equation shows that as weight increases the average MPG decreases, more specifically the average MPG decreases .007 for every 1 pound increase in weight. The y-intercept, 49.32, is the average MPG if the car weighed 0 pounds.
What is your r2 value? Is this a strong correlation? Why or Why not? If you are not sure, try searching the internet for supporting documents. Provide URL's for where you find your information
Answer: 0.704, not a strong correlation because the closer the number is to positive or negative one is a better correlation, thus .7 is not close to 1.
Based on all the information you have, has your belief about the relationship of the two categories changes? Why or why not?
Answer: No because we still believe that as the weight increases the MPG decreases. There is not possibly as strong of a relationship as we had thought, but there is a relationship.
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
How would you determine which equation had the best relationship?
Answer: Look at the graph of the data points and see which trendline was the most "in the middle" of the data points.
Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Answer: The linear option was NOT the optimal option. The better equation would be the power equation because it better approximates more of the data points. (Power Equation: y = 21042x^-.84)
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.
Back to Activity1Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using average MPG and another category that you feel may influence fuel efficiency. Answer the following questions.
Answer: We chose to compare average MPG and weight because we thought that the heavier the car the less the MPG would be.Identify the category you chose and why you thought there might be a relationship BEFORE creating the scatterplot?
Answer: X-axis is weight; y-axis is Average MPG. Weight is the independent variable and average MPG would be the variable that would be affected by the weight, thus the dependent variable.Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: Yes because as the weight increases, the average MPG is generally decreasing.Do you believe there is a relationship between the two categories? Why or why not?
Answer: Negative slope. This means that as one category increases the other category decreases.If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Regression Questions
(What is Regression?)
Create the linear regession equation in Excel, which Excel calls the trend line. Click the boxes to create both the equation and the r2 value on the graph. Answer the following questions.
Answer: y = -0.007x + 49.32; The equation shows that as weight increases the average MPG decreases, more specifically the average MPG decreases .007 for every 1 pound increase in weight. The y-intercept, 49.32, is the average MPG if the car weighed 0 pounds.What is your regression equation? Explain what the equation means in words.
Answer: 0.704, not a strong correlation because the closer the number is to positive or negative one is a better correlation, thus .7 is not close to 1.What is your r2 value? Is this a strong correlation? Why or Why not? If you are not sure, try searching the internet for supporting documents. Provide URL's for where you find your information
Answer: No because we still believe that as the weight increases the MPG decreases. There is not possibly as strong of a relationship as we had thought, but there is a relationship.Based on all the information you have, has your belief about the relationship of the two categories changes? Why or why not?
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
Answer: Look at the graph of the data points and see which trendline was the most "in the middle" of the data points.How would you determine which equation had the best relationship?
Answer: The linear option was NOT the optimal option. The better equation would be the power equation because it better approximates more of the data points. (Power Equation: y = 21042x^-.84)Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.
cars group 7.xls
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